Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks - the case studies of container terminals
- Submitting institution
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Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1207
- Type
- D - Journal article
- DOI
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10.1109/MCI.2015.2501552
- Title of journal
- IEEE Computational Intelligence Magazine
- Article number
- -
- First page
- 55
- Volume
- 11
- Issue
- 1
- ISSN
- 1556-603X
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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B - LOOM
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The method was tested in the field by the Dublin Ferryport (A. Colvin, Director, acolvin@dft.ie) and Radicatel (A. Bertelle, Director, aurelien.bertelle@critt-tl.fr), demonstrating that fleet size can be halved and significantly reduce costs. It has been used by VICONSHIP in their container handling operations, reducing costs by up to 23% (Dao Ngoc Hoan, IT Manager, hoan.it@viconship.com). The work has also led to an InnovateUK grant (ref. no. 971641, £265k to LJMU, 2019-2020). It led to a keynote speech at MSSS2020 (D. Zhang, Conference Chair, zhangdi@whut.edu.cn). This was one of only two papers accepted as Application Notes by this journal in 2016.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -